AR-009AltoFornitorePreventivo
Salvaguardie contro la deriva da autoapprendimento
Per i sistemi di IA che continuano ad apprendere dopo l'implementazione, i fornitori devono attuare salvaguardie che impediscano il degrado delle prestazioni attraverso cicli di retroazione, garantendo che qualsiasi autoapprendimento sia convalidato prima di influenzare i risultati.
Articoli:Article 15(3)Article 15(4)
Esempi di prove
- Continuous learning governance policy
- Self-learning validation gate procedure
- Feedback loop risk assessment
Norme
ISO 42001:2023 §8.4ISO/IEC 42001:2023 Annex B
Controlli correlati
- AR-001Accuracy Level Definition and CommitmentArticle 15(1) requires that high-risk AI systems are designed and developed to achieve an appropriate level of accuracy, robustness, and cybersecurity,…
- AR-002Accuracy Measurement and VerificationProviders must measure and verify the accuracy of the AI system against defined metrics using representative test sets, documenting the methodology,…
- AR-003Robustness Testing Against Input VariationsProviders must test the AI system's robustness against input variations that may arise in real-world deployment, including incomplete data, noisy inputs,…
- AR-004Adversarial Robustness TestingWhere relevant to the AI system's use case, providers must test robustness against adversarial inputs designed to cause misclassification, data poisoning…
- AR-005Cybersecurity Measures for AI SystemsArticle 15(5) requires that high-risk AI systems are resilient against unauthorised third-party attempts to alter their outputs, and providers must…
- AR-006Fallback Procedures for System FailuresProviders must design and implement fallback procedures that activate when the AI system cannot operate within its designed parameters, ensuring safe…
Termini correlati del Regolamento IA
- AccuracyThe requirement that high-risk AI systems achieve an appropriate level of accuracy in relation to their intended purpose, as specified in the technical documentation. Providers must declare the level of accuracy in the instructions for use.
- RobustnessThe ability of a high-risk AI system to maintain its level of performance under adverse conditions — including technical limitations, adversarial inputs, errors, or unexpected situations — or within foreseeable operating conditions outside the intended purpose.
- CybersecurityThe requirement that high-risk AI systems are resilient against attempts by third parties to alter their use, behaviour, or performance in ways that could result in risks to health, safety, or fundamental rights, including protection against data poisoning, adversarial examples, and model evasion attacks.
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